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The Primate Life History Database: a unique shared ecological data resource: Primate Life History Database

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1. The importance of data archiving, data sharing and public access to data has received considerable attention. Awareness is growing among scientists that collaborative databases can facilitate these activities.2. We provide a detailed description
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  The Primate Life History Database: a unique sharedecological data resource Karen B. Strier  1 *, Jeanne Altmann 2,11 , Diane K. Brockman 3 , Anne M. Bronikowski 4 ,Marina Cords 5 , Linda M. Fedigan 6 , Hilmar Lapp 7 , Xianhua Liu 7 , William F. Morris 8 ,Anne E. Pusey 9 , Tara S. Stoinski 10 and Susan C. Alberts 8,11 1 Department of Anthropology, University of Wisconsin-Madison, Madison, WI, USA;  2 Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA;  3 Department of Anthropology, University of NorthCarolina-Charlotte, Charlotte, NC, USA;  4 Department of Ecology, Evolution and Organismal Biology, Iowa StateUniversity, Ames, IA, USA;  5 Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USA;  6 Department of Anthropology, University of Calgary, Calgary, Canada;  7 National Evolutionary Synthesis Center, Durham, NC, USA;  8 Department of Biology, Duke University, Durham, NC, USA;  9 Department of Evolutionary Anthropology, Duke University, Durham, NC, USA;  10 The Dian Fossey Gorilla Fund International and Zoo Atlanta, Atlanta, GA, USA; and   11 Institute of Primate Research, National Museums of Kenya, Nairobi, Kenya Summary 1.  The importance of data archiving, data sharing and public access to data has received consider-able attention. Awareness is growing among scientists that collaborative databases can facilitatetheseactivities. 2.  We provide a detailed description of the collaborative life history database developed by ourWorking Group at the National Evolutionary Synthesis Center to address questions about lifehistorypatternsandtheevolutionofmortalityanddemographicvariabilityinwildprimates. 3.  Examplesfrom eachoftheseven primate species included inour databaseillustratethe rangeof data incorporated and the challenges, decision-making processes, and criteria applied to standard-izedataacrossdiversefieldstudies.Inadditiontothedescriptiveandstructuralmetadataassociatedwithourdatabase,wealsodescribetheprocessmetadata(howthedatabasewasdesignedanddeliv-ered)andthetechnicalspecificationsofthedatabase. 4.  Ourdatabaseprovidesausefulmodelforotherresearchersinterestedindevelopingsimilartypesof databases for other organisms, while our process metadata may be helpful to other groups of researchersinterestedindevelopingdatabasesforothertypesofcollaborativeanalyses. Key-words:  bioinformatics, data archiving, data sharing, database development, evolutionarybiology, population ecology Introduction The accumulation of long-term ecological data over the pastseveral decades, and increasing recognition of the need forbroad collaborative research efforts,present new challenges aswell as opportunities for the scientific community. The designandcurationofdatabasesthatcanaccommodatethecomplex,dynamic data sets typical of ecological research pose a practi-cal hurdle because of the enormous range of data that is ofteninvolved.Insomecases,thesedatasetsarehistoricandnotyetdigitized, but of great potential value nonetheless. More fre-quently,thedatasetsarecontemporary,andarecompletelyorpartiallydigitized,butinnon-standardizedways.Inadequaciesin the development and maintenance of these data sets maymake them largely inaccessible for the kinds of synthetic, col-laborative analyses that many ecological questions require(Cook et al  .2001;Michener2006).Incorporating data from multiple studies that span diversespecies and observational conditions intointegrated databasesfor comparative analyses is even more difficult to achievebecause differences in data collection and sampling methodsacross studiesmustfirstbereconciled, andastandardvocabu-lary must be developed based on common criteria. Identifyingand standardizing this vocabulary can be an arduous processthatreliesontheexpertiseofinvestigatorswithsufficientfamil-iaritywiththelong-termstudiestounderstandandexplainthenuances in their data sets (Nelson 2009). Yet, although thebenefits of archiving, sharing and increasing public access to *Correspondence author. E-mail: kbstrier@wisc.eduCorrespondence site: http://www.respond2articles.com/MEE/ Methods in Ecology & Evolution  2010,  1 , 199–211 doi: 10.1111/j.2041-210X.2010.00023.x   2010 The Authors. Journal compilation    2010 British Ecological Society  biological and ecological data have received considerableattention in the literature (e.g. Arzberger  et al.  2004; Parr &Cummings 2005; Piwowar  et al.  2008; Schofield  et al.  2009;Toronto International Data Release Workshop 2009), thereare still only a small number of published examples thatdescribe how synthetic, integrated databases are created andused (e.g. Ellison  et al.  2006; Even, Shankaranarayanan, &Watts2006;Jones et al. 2008).Here,wedescribethedevelopment,designandimplementa-tion of the Primate Life History Database (PLHD), a productof a larger collaborative endeavour jointly funded by theNational Evolutionary Synthesis Center (NESCent) and theNational Center for Ecological Analysis and Synthesis(NCEAS). The PLHD incorporates individual, longitudinallife history data from long-term field studies of wild primatesintoasyntheticdatabaseforcomparativeanalyses.Wepresentthe descriptive and structural metadata associated with thisdatabase, as well as the process metadata (i.e. a description of how the data set was designed and delivered). In addition toreviewing the content of the database and its underlying ratio-nale, we discuss the criteria we employed to standardize datafrom seven different field studies, and we provide examplesfrom the database to illustrate its design and technical specifi-cationsandtherangeofdataincluded.Our scientific motivation for the endeavour lies in ourshared interests in comparative analyses of primate life histo-ries,andspecifically,intheapplicationofdatafromwildpopu-lations to address questions about the evolutionary ecology of life histories. Age at first reproduction, fertility, longevity andother variables that influence both fitness and populationdynamics fluctuate in response to local ecological, social anddemographic conditions. Understanding this variability offersinsights not only into life history evolution, but also into theconservation and management of endangered species and theecologicalimpactsofglobalclimatechange(Strier et al. 2006).Primates are long-lived compared with most other mam-mals, and the decades of research required to document theirindividual lifehistories make theselongitudinal data irreplace-able resources. Yet, despite widespread recognition of thevalue of these data, primate researchers, like other ecologists,oftenlacktheexpertiseandresourcestoconstructthekinds of databases that facilitate data sharing and access, and that areneeded to protect data from the information loss that canoccur over time because of inadequate curation or mainte-nance (Michener 2006; Jones  et al.  2008). Our database pro-vides a useful model for other researchers interested indeveloping similar life history databases for other organisms.At the same time, the description of the process by which wedeveloped our database may be helpful to other groups of researchers interested in developing databases for other typesofcollaborativeanalyses. Materials and methods Our collaboration involved a multi-stage process. The first stageinvolved setting the agenda for the development of the PLHD(described in Strier  et al.  2006). This initial stage led to theformation of the core collaborative group, the Working Group,which included three sets of researchers. (i) Researchers represent-ing seven ongoing field studies of wild primates ranging from 24to 45 years in duration (Alberts, Altmann, Brockman, Cords,Fedigan, Pusey, Stoinski, Strier). (ii) Two evolutionary ecologistswith a particular interest in demography (Bronikowski, Morris).(iii) Two NESCent informatics specialists (Lapp, Liu). ThePLHD was developed by our Working Group over the course of three 4- to 5-day meetings held at NESCent in August 2007,January 2008 and August 2008.Thespeciesrepresentedbythesevenfieldstudiesaretaxonomicallydiverse. They include one indrid (Verreaux’s sifaka,  Propithecus ver-reauxi  , research ongoing for 24 years), two New World monkeys(white-faced capuchin,  Cebus capucinus , and northern muriqui, Brachyteles hypoxanthus ,studiesongoingfor25and27 years,respec-tively), two Old World monkeys (yellow baboons,  Papio cynocepha-lus , 37 years, and blue monkeys,  Cercopithecus mitis , 29 years) andtwo great apes (eastern chimpanzees,  Pan troglodytes schweinfurthii  ,45 years, and mountain gorillas,  Gorilla beringei beringei  , 41 years).All populations are wild, and with a few exceptions, no provisioningor interventions have occurred. Exceptions include veterinary inter-ventionandhistoricalprovisioninginthechimpanzeepopulation(i.e.almostdailyfrom1964to1967and1990to1996fordifferentcommu-nities, after which it was reduced and terminated altogether in 2000;Wrangham 1974; Goodall 1986; Pusey, Wilson, & Collins 2008), vet- erinary intervention in the gorilla population (Mudakikwa  et al. 2001),occasionalaccesstohuman-relatedfoodsbythebluemonkeys(Cords&Chowdhury,inpress)andoneinstanceinwhichresearchersrescued an infant muriqui and returned her to her mother (Nogueira et al. 1994).Development of the database itself occurred in two parallelprocesses. One process involved intensive discussion among theprimate researchers to design a shared terminology of attributes thatwould capture the relevant life history data for all seven species in astandardized manner. The goal was to permit us to address two cen-tral questions; one about the evolution of mortality across speciesand by sex, and the other about patterns of demographic variationacross species. Decisions about which data attributes to include andtheir relationships to one another were fairly straightforward, butdefining the meaning and constraints of each attribute operationally,in ways that made biological sense for all species and that accountedfordifferencesamongstudies,requiredpainstakingconsideration.Atthe same time, the informaticians conferred with the researchers todesign a database that presented the data as a series of three ‘views’,or virtual tables that represent organized, systematic abstractions of the underlying database. The three main views that resulted were: Biography  (Table S1, Supporting information);  Fertility Intervals (hereafter,  Fertility ; Table S2, Supporting information); and  StudyPopulation (Table S3,Supportinginformation). Biography  includes all live-born individuals (females and males) inour study populations, and is the core of our database. In the aggre-gate,  Biography  includes data that permit us to calculate the life spanforeachindividual (andtoidentify bothleft-truncatedand right-cen-sored records, i.e. cases in which individuals were already alive at theonset of observations or still alive when observations ceased, respec-tively). Because it also includes the identity of the mother for eachindividual (if known), we can use it to calculate fertility for eachmother identified in the database as well.  Fertility  identifies, for eachfemale, any interruptions in continuous observations that could haveresultedinmissedlivebirths.Together, Biography and  Fertility  allowtheusertodeterminethesequenceandnumberoflivebirthsthateachmother experienced during her lifetime (see ‘Database content’, for 200  K. B. Strier  et al.   2010 The Authors. Journal compilation    2010 British Ecological Society,  Methods in Ecology & Evolution ,  1 , 199–211  moredetails).The Study Population viewdesignatesadistinctivecodefor each study, which is used to identify the study for each individualinthe Biography and Fertility views.The database was populated in large batch uploads from spread-sheet-formatted data (see below under database design). A web-based graphical user interface was also developed to enable dataediting and entry by individual researchers and their collaborators.This interface is flexible yet comprehensive, and allows various lev-els of access control. For instance, while the database administra-tors have read and write access to all of the data, other users of thedatabase can be limited to read-only (search) access to one or someor all of the studies, or can be given edit privileges to one or severalstudies. This arrangement allows the administrators to grant appro-priate access to each Working Group member (i.e. each PrincipalInvestigator (PI) has read and write access to their own data, butonly read access to others’ data). It also allows research personnelfrom each study to gain access to that study’s data (but not to datafrom other studies), even if they did not participate in the workinggroup directly. Only the administrators can create users, and grantstudy-specific read or write access.Because all members of the Working Group would have access tothe entire database, we developeda Memorandumof Understanding(MOU) at our first meeting (http://demo.plhdb.org). We agreed thatPIscouldaddtheirowncollaboratorsasusersofthedatabaseforthespecieswithwhichtheywork,but accesstotheentiredatabaseiscur-rently limited to the Working Group members who have signed ourMOU. Database content Ourintentionwastohaveafairlysimplesetofrelationaltablesthat reflects the individual-based nature of our data. That is,foreachstudy,theindividualanimalistheunitofanalysis,andhence the set of all individual animals’ life history data com-prisethedataofinterest. Biography hasonerowforeachofthe3351 individuals across the seven studies. Each of the 17columns pertains to a life history variable or estimatesor ranges of error of these variables (Table S1, Supportinginformation).Measuringindividual(asopposedtopopulation-level)fertil-ity represents a special challenge in studies of wild animals,becauseunlikelongevity(measuredastheintervalbetweenthebirth and the death or disappearance of a known individual),individual fertility (measured, in our case, as the intervalbetween recorded live births) will be inaccurately estimated if even short gaps in observation result inmissed births. For thisreason, we recognized the need to identify, for each female ineach study, the periods during which we were reasonably surethat we had captured all births, and equivalently, the periodsduringwhichwewerenotabletoruleoutthepossibilitythatabirth and death of an infant had occurred during a gap inobservations.  Fertility  captures this information (Table S2,Supportinginformation).In addition, in  Study Population , we provide informationabout each study (location, species, etc.).  Study Population containsonerowforeachstudy( N   = 7;Table S3,Supportinginformation), identified by a unique, arbitrarilyassigned num-ber and to which all individuals represented in the other viewsforeachstudyarelinked. Data standardization We designed the relational database to permit us to addressspecific questions about the evolution of primate life histories,such as whether species and sexes age at similar rates (A.M.Bronikowski et al. ,unpublisheddata)andwhetherpopulationgrowth is more sensitive to female fertility vs. infant or adultsurvival (W.F. Morris  et al. , unpublished data). This meantidentifying the variables most critical to our analyses and thenreconciling variability in data coding, observation schedulesandconfidenceintervals for estimated dates toensurethat ourcriteria for assigning values were uniform and comparableacross studies. Differences inthebehaviour of theanimals andinlogisticalconditionsresultedinvariationindata-codingsys-tems both within individual studies and between the studies inthedatabase.Thesesourcesofvariationnecessitatedthedevel-opmentofacommonvocabulary,establishedamongWorkingGroupmembers,whichwouldensurethattermswereusedthesame way across studies. The common vocabulary is encom-passed by the terms defined below. These terms fall intothree different logical units: one belonging to the biographicalproperties of an animal ( Biography ), one belonging to thefertility properties of an animal ( Fertility ) and one belongingto the study population in which the animal lives ( StudyPopulation ). CONTENTS OF BIOGRAPHY StudyID Each of the different study populations, representing differentspeciesinourdatabase,wasassignedadistinctIDcode.  AnimID Because all of the data were individual-based, and the individ-ualwastheunitofanalysisinallstudies,theIDofeachanimal(typically an abbreviated code) in each study population wasthefundamentalunitofinformationaroundwhichallthedatawere organized. All individuals in each of the studies wereunambiguouslyidentifiablebytheirdistinctphysicalcharacter-isticsor,inonecase(sifaka),bytaggedcollarsandear-notches.Habituation to human observers facilitated the recognition of individuals. Within each study, there was a one-to-one rela-tionship between an animal’s ID code and the identity of anactual animal in each study population. However, AnimIDwas not auniquefield; animals in different studies mightshareanAnimID(forinstance,study2andstudy5bothhaveanani-malwithAnimID = ‘AFR’).Consequently,itisthecombina-tionofAnimIDandStudyIDthatproducesauniqueidentifierforeachanimalinthedatabase.  AnimName We included a column for the full name of each animal when-ever these had been assigned. This was included for complete-ness, and to enhance the ability of individual researchers to Primate Life History Database  201   2010 The Authors. Journal compilation    2010 British Ecological Society,  Methods in Ecology & Evolution ,  1 , 199–211  confirm the accuracy of all records associated with that indi-vidual. BirthGroup and BGCertainty  Our life history analyses were aimed primarily at species-leveldifferences. However, the social nature of primates in general,andthevariationintheirdispersalpatternsinparticular,ledusto include a column for specifying the social group into whichan animal was born (BirthGroup) and the researcher’s confi-dence in this assignment (BGCertainty). These variables willpermit us to collectively or individually evaluate whether thegroupofbirthcontributestolifehistoryvariance. Sex  Because many life history variables (e.g. age at maturity, dis-persal,lifespan)areknowntobesex-specific,wedistinguishedeachindividualinour databasebysex(Mor F).Occasionally,neonates that were born alive have died before researcherswere able to assign a sex; hence ‘U’ (for unknown) was anallowedvalueinthiscolumn. MomID This attribute corresponds to the AnimID of an individual’smother, when it was known. No value was assigned for indi-viduals whose mothers were unknown. MomID allows us, incombination with  Fertility  (see below) to measure fertility foreach mother recorded in the database. Also, by associatingeach individual with his or her mother’s AnimID, whenknown, we can evaluate how individual survivorship (andfemale fertility) relates to birth sequence and maternal age. AsisthecasewithAnimID,MomIDmustbeusedincombinationwithStudyIDtoidentifyauniquemother. FirstBorn Because age at first reproduction is a critical life history mar-ker, we distinguished whether individuals were known to betheirmother’sfirstoffspring. Birthdate, BDMin, BDMax  Birth dates, and estimates of the range of possible dates inwhich the birth could have occurred (BDMin and BDMax),are the key to estimating individual ages, and therefore neces-sary for all analyses of life histories. In some cases, continuityin field personnel and cohesive grouping patterns facilitateddaily or near-daily monitoring of all subjects, and the range of daysoverwhichBirthdatecouldbeestimatedwassmall.Whena mother was observed on sequential days, first without aninfant and subsequently with an infant, the Birthdate andBDmaxwereusuallyrecordedasoccurringonthesecondday,withtheBDmincorrespondingtoeitherthefirstorseconddaydepending on the study. In other cases, either gaps inobservations or the tendency of individuals to travel widelymade it difficult for observers to monitor all individuals regu-larly. Birthdate estimates in these circumstances were less pre-cise, and the difference between BDMin and BDMax wasmuchgreater.SomeBirthdateentries,includingthoseforanimalsthatwerealreadypresentattheonsetofobservationsoftheirpopulationor social group, were necessarily estimated on the basis of theindividual’svisiblesizeordevelopmentalcharacteristics.Thesebirthdateassignmentsforadultstendedtohavelargerintervalsbetween BDmin and BDmax estimates than animals first seenas immatures, because there were often few, if any, visibledifferencesbetweenyoung,middleagedorolderadultanimals.In these cases, the range of possible birth dates assigned(BDMinandBDMax)dependedontheresearchers’confidenceintheirestimates.Birthdateswereestimatedbasedonavarietyof traits in different species, including dental wear patterns atthetimeofcapture   ⁄   tagging(usedinsifaka),orvisiblephysicalsimilaritieswithadultsofknownages(usedinmoststudies).Inthe case of females, some species exhibit visible signs of parity,and researchers used these signs to estimate Birthdate andBDmin and BDmax from the average (±SD) age rangesknown for nulliparous and primiparous females in their studypopulations. Nonetheless, there were often still very largeranges of possible birth dates for some individuals; by havingthis information in the database, we could decide whether ornottoincludeparticularindividualsinspecificanalyses. BDDist  In a further effort to increase precision in birth date estimates,we assigned a birthdate distribution (BDDist) of Normal (N)when we considered the most likely birthdate to be closer toBirthdate than to BDMin or BDMax, and of Uniform (U)when any birthdate between BDMin and BDMax (includingBirthdate)wasequallylikely.Anormaldistributionoftheesti-mated birthdate was assigned if BDMin and BDMax repre-sented ±2 SD from the most likely Birthdate. Uniformdistributions were assigned if the probability distribution wastruncated at either BDMin or BDMax. For example, a newinfantobservedaftera30-daygapinobservationofitsmotherwould result in the infant’s BDMin on the last day its motherhad been observed and a BDMax 30 days later when it wasfirst observed. Based on the infant’s size and developmentupon first observation relative to other known infants in thestudy population, the researcher may have had good reasonsto estimate the infant’s Birthdate at either the midpoint of BDMin and BDMax, or else closer to either BDMin orBDMax. If estimated at the midpoint, then BDDist could beeither Normal or Uniform, but if not at the midpoint, theBDDistwouldnecessarilybeassignedasUniform. Entrydate and Entrytype Identifying the date at which individuals entered their respec-tive study populations allowed us to differentiate uncensoredobservations from left-truncated observations for survivalanalyses. Individuals were considered to enter their respective 202  K. B. Strier  et al.   2010 The Authors. Journal compilation    2010 British Ecological Society,  Methods in Ecology & Evolution ,  1 , 199–211  study populations at the time at which they could be individu-ally identified and close observations on them began. In mostcases, this corresponded to their birth. However, some indivi-duals immigrated into the study populations some time aftertheirbirth,andinthesecasesEntrydatecorrespondedtoimmi-gration date (or to confirmed AnimID via tagging in sifaka).Finally,allstudiesincludedindividualsthatwerepresentattheonset of the study itself or when close observations wereinitiatedonnewgroupsinthestudypopulation;inthesecases,Entrydate corresponded to the onset of the study or theindividual’sinclusioninthestudypopulation.Entrytypespecifiedeachofthefourpossiblewaysinwhichasubject could have entered the study population: birth (B);immigration (I); start of confirmed AnimID (C) and initiationof close observation (O). Although births and immigrationswereeasilyassigned,investigatorsdifferedintheirdesignationsof C and O. For example, in some cases an animal had beenrecognizable and familiar to the researchers based on occa-sional or opportunistic sightings before close observationbegan. In most cases, these animals only entered the databasewhen they immigrated (I) into one of the established studygroups. However, in other cases, they entered the databasebecause systematic observations were initiated on their group;in this case, either C or O could have been used. Eachresearcher described her assignments of C and   ⁄   or O in theirUser’s documentation, but these entry types are functionallyequivalent in terms of analyses, and should be treated as suchbydatabaseusers. Departdate and DepartdateError  The last date on which an animal was observed in the studypopulation is the Departdate. However, not all animals wereequally visible to observers on a daily basis, and observationschedules varied across the different studies and over time andbetween groups within studies. To capture the variation in thereliabilityofDepartdatesbetweenandwithinthedifferentstud-ies,we calculated the DepartdateError, which reflects the timebetweenDepartdate(lastdateobserved)andthefirsttimethatan animal was confirmed missing (e.g. when observationsresumed and all individuals present could be expected to bere-encountered). DepartdateError was expressed as a fractionofayear(numberofdaysdividedbynumberofdaysinayear),andwas>0wheneverthenumberofdaysbetweenDepartdateand retrospective confirmed missing date was >15 days. Insome studies, members of the study population did not live incohesivegroups,makingitdifficulttospecifyanexpectedlagtore-sighting and a corresponding DepartdateError. In caseswhen DepartdateError could not be calculated, its value wasmissing. Departtype SimilartoEntrytype,wedistinguishedfourDeparttypes:death(D); emigration (E); permanent disappearance (P) and the endof observation (O), which means that the individual was stillpresent at the most recent census date. Observations of deathsand the recovery of identifiable corpses in most primate habi-tats areextremely rare, but we nonetheless required strong cir-cumstantial evidence, such as visibly poor health or othermortality risks, or violations of population-specific behaviourpatterns, before assigning D. For example, the sudden perma-nent disappearance of an animal of the typically non-dispers-ing sex for that population could have been assigned aDeparttype of D, even in the absence of a corpse or other cir-cumstantial evidence. If the animal that disappeared was amember of the dispersing sex, then D was allowed when thedisappearance occurred before the youngest known dispersalage in that population, subject to the researchers’ expert opin-ion. Additional information, such as locations associated withhigh risk, were also considered when assigning D in theabsenceofobserveddeathoridentifiablecorpse.D was never assigned based solely on inferred risks associ-ated with age, and E was never assigned solely on the basis of the disappearance of an individual at the appropriate age andsex for dispersal. To assign E, researchers had to be confidentthat the individual had emigrated even if its subsequent fatewas not known. All disappearances that could not be attrib-uted to D or E were assigned P in the database. In demo-graphic analyses, P, E and O all function to signal right-censored observations. The four types of Departtypes areequivalenttoStoptypein Fertility . CONTENTS OF F ertility StudyID See Biography .  AnimID See Biography . Startdate and Stopdate The most difficult task in constructing  Fertility  was the devel-opment of standardized criteria for what constituted suffi-ciently continuous observations of a female to merit inclusionof that period, vs. gaps in observations that would be longenoughtohavepossiblyresultedinamissedbirthrecord.Eachrow in  Fertility  corresponded to one uninterrupted period of observation on a female (an interval during which no possiblebirthswouldhavebeenmissed).Eachfemaleforwhichatleastone such uninterrupted period was obtained was representedbyoneormorerowsin Fertility .Inadditiontothevariationintheobservationschedulesforeachstudy,someoftheprimatesin our database are elusive and impossible to monitor on adailybasis.Eachoftheprimatologistsprovideddetaileddocu-mentationaboutthecriteriatheyapplied. Starttype and Stoptype See Entrytype and Departype in  Biography ; these correspondto Starttype and Stoptype in  Fertility , where they defined the Primate Life History Database  203   2010 The Authors. Journal compilation    2010 British Ecological Society,  Methods in Ecology & Evolution ,  1 , 199–211
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